The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

当可用的硬件无法满足内存和计算要求以有效地训练高性能的机器学习模型时，需要妥协训练质量或模型复杂性。在联合学习（FL）中，节点是比传统服务器级硬件更具限制的数量级，并且通常是电池供电的，严重限制了可以在此范式下训练的模型的复杂性。尽管大多数研究都集中在设计更好的聚合策略上以提高收敛速度并减轻FL的沟通成本，但更少的努力致力于加快设备培训。这样的阶段重复数百次（即每回合）并可能涉及数千个设备，这是培训联合模型所需的大部分时间，以及客户端的全部能源消耗。在这项工作中，我们介绍了第一个研究在FL工作负载中培训时间引入稀疏性时出现的独特方面的研究。然后，我们提出了Zerofl，该框架依赖于高度稀疏的操作来加快设备训练。与通过将最先进的稀疏训练框架适应FL设置相比，接受Zerofl和95％稀疏性训练的模型高达2.3％的精度。

启用摄像头的移动设备的无处不在导致在边缘生产大量未标记的视频数据。尽管已经提出了各种自我监督学习（SSL）方法来收集其潜在的时空表征，以进行特定于任务的培训，但实际挑战包括隐私问题和沟通成本，可以阻止SSL在大规模上部署。为了减轻这些问题，我们建议将联合学习（FL）用于视频SSL的任务。在这项工作中，我们评估了当前最新ART（SOTA）视频-SSL技术的性能，并确定其在与Kinetics-400数据集模拟的大规模FL设置中集成到大规模的FL设置时的缺陷。我们遵循，为视频（称为FedVSSL）提出了一个新颖的Federated SSL框架，该框架集成了不同的聚合策略和部分重量更新。广泛的实验证明了FEDVSSL的有效性和意义，因为它在UCF-101上优于下游检索任务的集中式SOTA，而HMDB-51的效率为6.66％。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

支持麦克风的设备的无处不在导致在边缘生产大量未标记的音频数据。自我监督学习（SSL）和联合学习（FL）的整合到一个连贯的系统中，可以提供数据隐私保证，同时还可以提高语音表示的质量和稳健性。在本文中，从算法，硬件和系统限制的角度来看，我们对FL场景下的培训语音SSL模型的可行性和复杂性提供了首个系统研究。尽管它们的组合具有很高的潜力，但我们发现现有的系统限制和算法行为使SSL和FL系统几乎无法构建。然而，至关重要的是，我们的结果表明了特定的绩效瓶颈和研究机会，这将使这种情况得到逆转。尽管我们的分析表明，鉴于硬件的现有趋势，混合SSL和FL语音系统要等到2027年才能可行。我们认为，这项研究可以成为加速工作以提早达到这一里程碑的路线图。

解决SCHR \“Odinger方程是许多量子力学性能的关键。然而，分析解决方案仅用于单电子系统的易行。最近，神经网络在许多电子系统的建模波函数中成功。与变分蒙特 - Carlo（VMC）框架，这导致了与最着名的经典方法相提并论的解决方案。仍然，这些神经方法需要大量的计算资源，因为一个人必须为每个分子几何训练单独的模型。在这项工作中，我们结合了一个图形神经网络（GNN）具有神经波功能，同时通过VMC解决多个几何的SCHR \“Odinger方程。这使我们能够通过单个训练通过模拟潜在能量表面的连续子集。与现有的最先进的网络相比，我们的潜在能量表面网络PESNet在匹配或超越其准确性的同时将多个几何形状的训练速度加速至多40次。这可以打开准确和数量级的路径便宜的量子力学计算。

联合学习（FL）作为边缘设备的有希望的技术，以协作学习共享预测模型，同时保持其训练数据，从而解耦了从需要存储云中的数据的机器学习的能力。然而，在规模和系统异质性方面，FL难以现实地实现。虽然有许多用于模拟FL算法的研究框架，但它们不支持在异构边缘设备上进行可扩展的流程。在本文中，我们呈现花 - 一种全面的FL框架，通过提供新的设施来执行大规模的FL实验并考虑丰富的异构流程来区分现有平台。我们的实验表明花卉可以仅使用一对高端GPU在客户尺寸下进行FL实验。然后，研究人员可以将实验无缝地迁移到真实设备中以检查设计空间的其他部分。我们认为花卉为社区提供了一个批判性的新工具，用于研究和发展。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.